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Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation

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Abstract

This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well-defined cluster, which allows the study of the characteristics of this type of events.

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Acknowledgements

This work has been subsidised by the Project TIN2014-54583-C2-1-R of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7508 Project of the Junta de Andalucía (Spain). Antonio M. Durán-Rosal’s research has been subsidised by the FPU Predoctoral Program (Spanish Ministry of Education and Science), Grant reference FPU14/03039. Manuel Dorado-Moreno’s research has been subsidised by the FPU Predoctoral Program (Spanish Ministry of Education and Science), Grant reference FPU15/00647.

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein building, 14071, Córdoba, Spain

    Antonio M. Durán-Rosal, Manuel Dorado-Moreno, Pedro A. Gutiérrez & César Hervás-Martínez

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  1. Antonio M. Durán-Rosal
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  2. Manuel Dorado-Moreno
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  3. Pedro A. Gutiérrez
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  4. César Hervás-Martínez
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Correspondence to Antonio M. Durán-Rosal.

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Durán-Rosal, A.M., Dorado-Moreno, M., Gutiérrez, P. . et al. Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation. Prog Artif Intell 6, 59–66 (2017). https://doi.org/10.1007/s13748-016-0105-1

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